Dyadic measurement invariance and its importance for replicability in romantic relationship science
Bibliographic record
Abstract
Abstract Comparisons of group means, variances, correlations, and/or regression slopes involving psychological variables rely on an assumption of measurement invariance—that the latent variables under investigation have equivalent meaning and measurement across group. When measures are noninvariant, replicability suffers, as comparisons are either conceptually meaningless, or hindered by inflated Type I error rates. We propose that the failure to account for interdependence among dyad members when testing measurement invariance may be a potential source of unreplicable findings in relationship research. We developed fully dyadic versions of invariance models, created an R package ( dySEM ) to make specifying dyadic invariance models easier and reporting more reproducible, and executed a Registered Report for gauging the extent of dyadic (non)invariance in romantic relationship research across measures of relationship well‐being, personality, and sexuality in a sample of 282 heterosexual couples. We found that although a number of popular measures display good evidence of dyadic invariance, a few display concerning levels and interesting patterns of noninvariance, while others appeared either noninvariant or poorly fitting for both men and women. We discuss our findings in terms of their meaning for the replicability dyadic close relationship research. We close by arguing that increased theorizing and research on dyadic invariance, and inclusive methods for analyzing invariance with indistinguishable dyads, are needed to capitalize on the opportunity to advance our field's understanding of dyadic constructions of relational concepts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.240 | 0.541 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".